Enabling health, independence and wellbeing for psychiatric patients through personalised ambient monitoring (PAM)

十分之一的英国人会在一生中的某个阶段遭受致残性焦虑症的困扰，而每年有9100万个工作日因心理健康问题而损失，英国为此付出的生产力损失和治疗费用高达320亿英镑。除了整个国家的财政负担之外，当然还有个人及其家人和朋友的个人成本(www.mind.org.uk)。最近，负责卫生服务的国务部长罗西·温特顿宣布，政府将帮助患有癌症或精神健康问题等长期疾病的人保持独立，并通过提供药物信息来控制他们的疾病。PAM(个性化环境监测)将更进一步，允许患者选择现成的技术来监测他们的活动特征；行为模式的测量表明人们的心理健康状态。PAM将在一个人的家中使用一组多个谨慎的传感器，并将其连接到一个编程为检测活动特征变化的计算机系统。然后，这些可以用来向患者、他们的家人或他们的医生发出自动警报，从而提供避免虚弱发作的能力。这项初步研究将探索减少虚弱发作发生率的可行性。这将通过纵向监测来实现，以便及早发现健康状况恶化并防止可避免的住院。这项工作将以南安普顿大学(ISVR和CORMSIS)在这一领域的现有工作为基础(其中包括即将向EPSRC提出关于将类似的相关技术应用于康复监测的建议)。诺丁汉大学的投入将扩展目前在传统和职业健康领域的生理监测工作，而他们参与EPSRC资助的卫生技术评估IMRC将提供一种机制，潜在地评估该项目的真正价值。斯特林大学已经通过Match在这一领域开展业务，并拥有稳定的中间件平台，但在该项目的早期阶段，我们将评估该平台，特别是我们将与EPSRC Equator项目联系，讨论为用于分布式健康监测演示而开发的中间件的使用。调查人员提供跨医疗设备和传感器、医疗信号处理、通信和软件服务以及运筹学建模的技能和经验。这种混合对于项目的成功是必不可少的，并提供了一个有趣和独特的跨学科分组。IDEAS沙坑提供了形成这种积极关系的独特机会。交付内容：医疗保健：-在精神保健背景下对环境监测的评估-改善这一患者群体的健康、独立和幸福状况-支持与药物的一致性和对药物的更好理解(S)技术：-能够简单地插入和打开传感器-用于家庭的低成本、坚固的传感器-集成到例如衣服、个人设备(例如移动电话、MP3播放器)、常规交通方式中的新型传感器。它们都有可能提供有关“活动”的数据。-智能实时算法，从不同和稀疏的数据中识别行为特征。-展示在(可能)与传感器断断续续连接的移动电话上建立漫游网关的可能性。

One in ten of the UK population will suffer a disabling anxiety disorder at some stage in their life whilst 91 million working days are lost per year due to mental health problems and the cost to the country is 32 billion in lost productivity and treatment costs. In addition to this financial burden to the country as a whole there are of course the personal costs to individuals, their family and friends (www.mind.org.uk). Recently Rosie Winterton, the Minister of State for Health Services, announced the Government will help people with long-term conditions such as cancer, or mental health problems, to stay independent and take control of their illness by providing information with medication. PAM (personalised ambient monitoring) will take this even further by allowing patients to select off-the-shelf technology that will monitor their activity signatures ; measurements of behavioural patterns which indicate people's mental health state. PAM will use a set of multiple discreet sensors in a person's home, coupled to a computer system programmed to detect changes in activity signatures. These can then be used to issue automatic alerts to the patient, their family, or their doctor, thus providing the capability to avert debilitating episodes.This pilot study will investigate the feasibility of reducing the incidence of debilitating episodes. This will be achieved by longitudinal monitoring that will permit early detection of deteriorating health and prevent avoidable hospital admissions. This work will build on existing work in this area at the University of Southampton (ISVR and CORMSIS) (that includes an imminent proposal to EPSRC on similar related technology applied to rehabilitation monitoring). University of Nottingham input will extend current work in physiological monitoring in both the conventional and vocational health domains whilst their involvement in the EPSRC funded health technology assessment IMRC will provide a mechanism to potentially assess the project's true value. The University of Stirling already operates in this domain via MATCH and has a stable middleware platform, however at an early stage in this project we will assess the platform, and in particular we will liaise with the EPSRC EQUATOR project regarding use of middleware developed for used in a distributed health monitoring demonstrator.The investigators provide skills and experience across medical devices and sensors, medical signal processing, communications and software services, and Operational Research modelling. This mix is essential to the success of the project, and provides an interesting and distinct interdisciplinary grouping. The IDEAS sandpit has provided a unique opportunity to form such a positive relationship.Deliverables:To healthcare:- An evaluation of ambient monitoring in a mental healthcare context- Improved health, independence and wellbeing status for this patient group- To support concordance with medication and better understanding of the drug(s)To technology:- The ability to simply plug in and turn on a sensor- Low-cost, robust sensors for use in the home- Novel sensors incorporated into, for example, clothing, personal devices (e.g. mobile phones, MP3 players), regular modes of transport. All have the potential to provide data on 'activity'.- Intelligent real-time algorithms to identify behavioural signatures from disparate and sparse data.- Show the potential for a roving gateway on (possibly) a mobile phone working with intermittent connectivity to sensors.